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Science:首个AI设计的噬菌体病毒诞生,能够存活、感染宿主,还能对抗抗生素耐药难题

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TL;DR - Arc Institute/Stanford's Brian Hie team published in Science (Aug 6, 2026; bioRxiv Sept 2025) the first generative design of complete bacteriophage genomes using genome language models (Evo 1/Evo 2), producing viable phages that infect E. coli. It marks a shift from single-gene/protein design to whole-genome-scale generative biology.

  • Used ΦX174 (~5.4 kb, 11 genes, ≥7 regulatory elements, 2 recognition sequences) as the design template; thousands of AI-generated genomes were computationally evaluated, ~300 chemically synthesized, and 16 viable phages recovered.
  • Generated phages differ from all known natural phages: de novo mutations, differentiated genes/regulatory elements, and varied genome lengths; cryo-EM showed one used a DNA-packaging protein from an evolutionarily distant phage in its capsid.
  • A cocktail of generated phages rapidly overcame E. coli strains resistant to natural ΦX174, whereas a natural ΦX174-like phage cocktail did not — suggesting a route to adaptive phage therapy against antibiotic-resistant/fast-evolving pathogens.
  • Demonstrates genome language models capture evolutionary constraints in DNA with enough fidelity for genome-scale design, laying groundwork for larger, more complex synthetic genomes.

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Science:首个AI设计的噬菌体病毒诞生,能够存活、感染宿主,还能对抗抗生素耐药难题

WeChat: 生物世界 2026-08-08 doi:10.1126/science.aec2657
Public signals OpenAlex citations 5
Providers: Hugging Face · N/A OpenAlex · Citations 5 Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-09 08:18:15.560326 UTC

TL;DR - Arc Institute/Stanford's Brian Hie team published in Science (Aug 6, 2026; bioRxiv Sept 2025) the first generative design of complete bacteriophage genomes using genome language models (Evo 1/Evo 2), producing viable phages that infect E. coli. It marks a shift from single-gene/protein design to whole-genome-scale generative biology.

  • Used ΦX174 (~5.4 kb, 11 genes, ≥7 regulatory elements, 2 recognition sequences) as the design template; thousands of AI-generated genomes were computationally evaluated, ~300 chemically synthesized, and 16 viable phages recovered.
  • Generated phages differ from all known natural phages: de novo mutations, differentiated genes/regulatory elements, and varied genome lengths; cryo-EM showed one used a DNA-packaging protein from an evolutionarily distant phage in its capsid.
  • A cocktail of generated phages rapidly overcame E. coli strains resistant to natural ΦX174, whereas a natural ΦX174-like phage cocktail did not — suggesting a route to adaptive phage therapy against antibiotic-resistant/fast-evolving pathogens.
  • Demonstrates genome language models capture evolutionary constraints in DNA with enough fidelity for genome-scale design, laying groundwork for larger, more complex synthetic genomes.
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